What’s the Deal with California?

Introduction

As you may have noticed from the Contact Toxicity By State graph (located at the bottom), California had a really high toxic load. Why is that? Let’s look at some other visualizations of our data to find an answer.

What are we looking for?

First, let’s look at what kind of crops we have in CA as of 2014:

Now let’s see how intense these crops were measured in toxic load per acre.

Insecticide Intensity for each crop - California 2014

Pasture and hay occupies the most acreage, but Cotton has the highest Contact toxic load per acre of insecticide applied, followed by Other Crops, Vegetables and Fruit, then Orchards and Grapes. When we look at the Oral toxic load, we see the same 4 crops rank in the highest toxic load per acre measurements.

Although these four crops occupy a smaller area, they require a lot more insecticide applications and thus have a higher toxic load per acre value. But they don’t look like they occupy that much of the total crop area (see first plot).

Let’s look at the total crop area graph again, but this time without pasture and hay:

Total Crop Area Without Pasture and Hay

In this adjusted total crop area plot, we see that Orchards and Grapes account for a huge chunk of crop area in California! They are only second to Pasture and Hay!

Once we remove Pasture and Hay, we can see that Orchards and Grapes account for much more area than we may have originally thought. Given this fact and the fact that insecticides used on Orchards and Grapes are some of the strongest (3rd highest toxic load per acre), it makes sense why California had a such a high toxic load for the entire state: It’s because of the high concentration of Orchard and Grape crops! In fact, about 80% of all fruits and vegetables for the nation are grown in California.

Session information

sessionInfo()

R version 3.5.0 (2018-04-23)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 16299)

Matrix products: default

locale:
[1] LC_COLLATE=English_United States.1252 
[2] LC_CTYPE=English_United States.1252   
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] bindrcpp_0.2.2  scales_0.5.0    forcats_0.3.0   stringr_1.3.1  
 [5] purrr_0.2.5     readr_1.1.1     tidyr_0.8.1     tibble_1.4.2   
 [9] ggplot2_2.2.1   tidyverse_1.2.1 dplyr_0.7.5    

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.4  reshape2_1.4.3    haven_1.1.2      
 [4] lattice_0.20-35   colorspace_1.3-2  htmltools_0.3.6  
 [7] yaml_2.1.19       rlang_0.2.1       R.oo_1.22.0      
[10] pillar_1.2.3      foreign_0.8-70    glue_1.2.0       
[13] R.utils_2.6.0     modelr_0.1.2      readxl_1.1.0     
[16] bindr_0.1.1       plyr_1.8.4        munsell_0.5.0    
[19] gtable_0.2.0      workflowr_1.1.1   cellranger_1.1.0 
[22] rvest_0.3.2       R.methodsS3_1.7.1 psych_1.8.4      
[25] evaluate_0.10.1   labeling_0.3      knitr_1.20       
[28] parallel_3.5.0    broom_0.4.4       Rcpp_0.12.17     
[31] backports_1.1.2   jsonlite_1.5      mnormt_1.5-5     
[34] hms_0.4.2         digest_0.6.15     stringi_1.1.7    
[37] grid_3.5.0        rprojroot_1.3-2   cli_1.0.0        
[40] tools_3.5.0       magrittr_1.5      lazyeval_0.2.1   
[43] crayon_1.3.4      whisker_0.3-2     pkgconfig_2.0.1  
[46] xml2_1.2.0        lubridate_1.7.4   rstudioapi_0.7   
[49] assertthat_0.2.0  rmarkdown_1.10    httr_1.3.1       
[52] R6_2.2.2          nlme_3.1-137      git2r_0.22.1     
[55] compiler_3.5.0